Summary statistics for a single indicator+entity: n, mean, median, std, min/max, quartiles, skew, histogram. Use FIRST before running any test so you know what the data looks like (sample size, completeness, distribution shape).
| Name | Required | Description | Default |
|---|---|---|---|
| time | No | ||
| entity | Yes | ||
| indicator | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey read-only, idempotent, and non-destructive behavior. The description adds no additional behavioral traits beyond stating it computes summary statistics. It does not discuss performance, rate limits, or any side effects. Since annotations carry the full burden and description adds minimal extra transparency, a score of 2 is appropriate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences that efficiently convey the purpose, output, and usage guidance. It is well-structured with the core function stated first, followed by a clear user directive. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the main output and usage intent but omits details about the optional 'time' parameter and does not explain how time filtering affects the statistics. With no output schema, the description could be more explicit about the return format. Overall, it is adequate for a simple descriptive tool but has gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, meaning the input schema has no descriptions. The description only mentions 'indicator' and 'entity' by name but provides no explanation of their meaning, valid values, or format. The 'time' parameter is not mentioned at all. The description fails to compensate for the lack of schema descriptions, resulting in very poor parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides summary statistics for a single indicator+entity and lists the specific statistics returned (n, mean, median, std, min/max, quartiles, skew, histogram). It also positions the tool as a preliminary step, distinguishing it from sibling tools by advising to use it 'FIRST' before any test.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly tells when to use the tool: 'Use FIRST before running any test' and explains the purpose (know sample size, completeness, distribution shape). It does not provide explicit when-not-to-use or list alternatives, but the guidance is clear and actionable, justifying a 4.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are strongly domain-specific with clear boundaries, especially the 360 reports and dataset/chart CRUD tools. Some overlap exists around driver analysis (find_drivers, what_matters, decompose_drivers) and dataset discovery (search_datasets, discover_by_topic, list_indicators), but the descriptions make the intended use cases mostly distinguishable.
The vast majority of tools follow a clear snake_case verb_noun or get_noun pattern, e.g. list_connectors, refresh_connector, query_dataset, delete_dataset. Minor deviations such as calculate, describe, bubble_or_not, what_matters, and the 360-style report names keep it from being perfectly uniform.
48 tools is far beyond the 3-15 range and even beyond the 25-tool threshold for a heavy surface. The platform is broad and the tools are organized into domains, but the sheer number creates a high selection burden for an agent and suggests the server is trying to cover too many workflows in one toolset.
The toolset covers dataset lifecycle, chart lifecycle, data discovery, querying, statistics, app context, connectors, and admin reports remarkably well. Notable gaps are the lack of a delete_chart tool and no row-level update/delete for datasets, but agents can generally work around these or treat them as intentional platform constraints.
